This paper proposes two accelerated gradient descent algorithms for systems with missing input data with the aim at achieving fast convergence rates. Based on the inverse auxiliary model, the missing ...

Mini Batch Gradient Descent is an algorithm that helps to speed up learning while dealing with a large dataset. Instead of updating the weight parameters after assessing the entire dataset, Mini ...

Another famous way of fast convergence is to increase the batch size adaptively. This paper proposes a new optimization technique named adaptive diff-batch or adadb that removes the problem of ...

You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs ...